Interpretability and explainability are related concepts in the field of machine learning and artificial intelligence, but they have different focuses. Interpretability refers to how easily a human can understand the model's decisions, while explainability pertains to the methods and tools used to provide reasoning for those decisions. In essence, interpretability is about the model itself being straightforward enough that its output can be directly understood, whereas explainability involves providing information that clarifies or elucidates those decisions.
For example, consider a linear regression model that predicts housing prices based on features like square footage, location, and age of the property. This type of model is interpretable because developers can look at the coefficients (weights) assigned to each feature and understand how each one affects the prediction. If the model predicts that a house will cost $300,000 and the square footage coefficient is $150 per square foot, it’s clear how the model arrived at that number. Developers can directly interpret how much each feature contributes to the prediction, making it transparent.
On the other hand, if you are working with a complex model like a deep neural network, it may not be as interpretable due to its intricate structure. In such cases, explainability comes into play. You might use techniques like LIME (Local Interpretable Model-Agnostic Explanations) or SHAP (SHapley Additive exPlanations) to explain the model's outputs. These methods provide insights into how different features influence specific predictions, even though the model itself is too complicated to interpret directly. Thus, while interpretability emphasizes direct understanding of the model’s components, explainability focuses on methods to clarify and rationalize those outputs when direct interpretation isn’t feasible.